지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.6
- 발행연도
- 2023.12
- 수록면
- 495 - 501 (7page)
- DOI
- 10.5573/IEIESPC.2023.12.6.495
이용수
초록· 키워드
오류제보하기
Dance videos contain numerous complex movements that make movement recognition difficult. The multi-feature fusion method proposed in this paper first takes color and texture features of video frames for clustering, and then extracts key frames. Based on the key frames, spatial and temporal features, respectively, are extracted by using long short-term memory and a three-dimensional convolutional neural network. A multi-feature fusion method designed for movement recognition is used in experiments conducted with both the DanceDB dataset and a selfbuilt dataset. The results show that the proposed multi-feature fusion method has high recall and precision ratios for key frame extraction, and achieved recognition accuracy of 42.67% and 50.64% with DanceDB and the self-built dataset, respectively. This paper validates the effectiveness of the proposed method for key frame extraction and movement recognition in dance videos, and suggests potential practical applications. The approach improves the reliability of video processing and provides theoretical support for further research on deep learning methods in the field of video processing.
목차
Abstract
1. Introduction
2. Multi-feature Fusion Method for Movement Recognition
3. Results and Analysis
4. Conclusion
References
참고문헌 (23)
참고문헌 신청
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J. Yang, Y. Jiang, and S. Wang, “Enhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing,” in ICC 2022 - IEEE International Conference on Communications, Seoul, Korea, Republic of, pp. 739-744, May. 2022.
M. Majd, and R. Safabakhsh, “A motion-aware ConvLSTM network for action recognition,” Applied Intelligence, Vol. 49, No. 7, pp. 1-7, 2019. Article (CrossRef Link)
K. Zhang, and W. Ling, “Joint Motion Information Extraction and Human Behavior Recognition in Video Based on Deep Learning,” IEEE Sensors Journal, Vol. 20, No. 20, pp. 11919-11926, 2020. Article (CrossRef Link)
T. Hoshino, S. Kanoga, M. Tsubaki, and A. Aoyama, “Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers - ScienceDirect,” Neurocomputing, Vol. 489, pp. 599-612, 2022. A
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